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Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework
Pan, Zhengke1,2; Liu, Pan1,2; Gao, Shida1,2; Xia, Jun1,2,3; Chen, Jie1,2; Cheng, Lei1,2
2019-08-19
Source PublicationHYDROLOGY AND EARTH SYSTEM SCIENCES
ISSN1027-5606
Volume23Issue:8Pages:3405-3421
Corresponding AuthorLiu, Pan(liupan@whu.edu.cn)
AbstractUnderstanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. Many existing studies model the time-varying parameters as functions of physically based covariates; however, a major challenge remains in finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes and one scenario with a stationary scheme for model parameters were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine the validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with the stationary setting of model parameters, (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance, and (3) model parameters calibrated over dry years were not suitable for predicting runoff over wet years because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.
DOI10.5194/hess-23-3405-2019
WOS KeywordMODEL PARAMETERS ; UNGAUGED CATCHMENTS ; NON-STATIONARITY ; RUNOFF ; RAINFALL ; REGIONALIZATION ; UNCERTAINTY ; STREAMFLOW ; TRANSFERABILITY ; IDENTIFICATION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program[2018YFC0407202] ; National Natural Science Foundation of China[51861125102] ; National Natural Science Foundation of China[51879193] ; Natural Science Foundation of Hubei Province[2017CFA015] ; Innovation Team in Key Field of the Ministry of Science and Technology[2018RA4014]
Funding OrganizationNational Key Research and Development Program ; National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province ; Innovation Team in Key Field of the Ministry of Science and Technology
WOS Research AreaGeology ; Water Resources
WOS SubjectGeosciences, Multidisciplinary ; Water Resources
WOS IDWOS:000481989200002
PublisherCOPERNICUS GESELLSCHAFT MBH
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68751
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiu, Pan
Affiliation1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Hubei, Peoples R China
2.Wuhan Univ, Hubei Prov Key Lab Water Syst Sci Sponge City Con, Wuhan, Hubei, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Pan, Zhengke,Liu, Pan,Gao, Shida,et al. Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework[J]. HYDROLOGY AND EARTH SYSTEM SCIENCES,2019,23(8):3405-3421.
APA Pan, Zhengke,Liu, Pan,Gao, Shida,Xia, Jun,Chen, Jie,&Cheng, Lei.(2019).Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework.HYDROLOGY AND EARTH SYSTEM SCIENCES,23(8),3405-3421.
MLA Pan, Zhengke,et al."Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework".HYDROLOGY AND EARTH SYSTEM SCIENCES 23.8(2019):3405-3421.
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